In [ ]:
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.show()
<Figure size 640x480 with 0 Axes>
import helper
import cv2
# Loading Image
from skimage.io import imread,imshow,imsave
import numpy as np
from matplotlib import pyplot as plt
img = imread("barbara_gray.bmp")
def PSNR(img1, img2):
MSE = np.sum((img1 - img2) ** 2) / (img1.shape[0] * img1.shape[1])
if MSE == 0:
return 100
PIXEL_MAXVALUE = 255.0
return 10 * np.log10(PIXEL_MAXVALUE**2 / MSE)
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.show()
<Figure size 640x480 with 0 Axes>
# 5% corrupted image
noise_image_5 = helper.salt_pepper_noise(img,0.05)
removed_noise_5 = helper.median_filter(noise_image_5,5)
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(noise_image_5,cmap=plt.get_cmap('gray'))
plt.title("noise_image_5")
plt.subplot(1,3,3)
plt.imshow(removed_noise_5,cmap=plt.get_cmap('gray'))
plt.title("removed_noise_5")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# 15% corrupted image
noise_image_15 = helper.salt_pepper_noise(img,0.15)
# Remove noise from the image
removed_noise_15 = helper.median_filter(noise_image_15,5)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(noise_image_15,cmap=plt.get_cmap('gray'))
plt.title("noise_image_15")
plt.subplot(1,3,3)
plt.imshow(removed_noise_15,cmap=plt.get_cmap('gray'))
plt.title("removed_noise_15")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# 20% corrupted image
noise_image_20 = helper.salt_pepper_noise(img,0.20)
# Remove noise from the image
removed_noise_20 = helper.median_filter(noise_image_20,7)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(noise_image_20,cmap=plt.get_cmap('gray'))
plt.title("noise_image_20")
plt.subplot(1,3,3)
plt.imshow(removed_noise_20,cmap=plt.get_cmap('gray'))
plt.title("removed_noise_20")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# 25% corrupted image
noise_image_25 = helper.salt_pepper_noise(img,0.25)
# Remove noise from the image
removed_noise_25 = helper.median_filter(noise_image_25,9)
removed_noise_257 = helper.median_filter(noise_image_25,7)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,4,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,4,2)
plt.imshow(noise_image_25,cmap=plt.get_cmap('gray'))
plt.title("noise_image_25")
plt.subplot(1,4,3)
plt.imshow(removed_noise_25,cmap=plt.get_cmap('gray'))
plt.title("removed_noise_25")
plt.subplot(1,4,4)
plt.imshow(removed_noise_257,cmap=plt.get_cmap('gray'))
plt.title("removed_noise_25_7")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
#Psnr
print("PSNR for 5% noise image is ",cv2.PSNR(noise_image_5,removed_noise_5))
print("PSNR for 15% noise image is ",cv2.PSNR(noise_image_15,removed_noise_15))
print("PSNR for 20% noise image is ",cv2.PSNR(noise_image_20,removed_noise_20))
print("PSNR for 25% noise image is ",cv2.PSNR(noise_image_25,removed_noise_25))
print("PSNR for 25% with kernal size 7*7 noise image is ",cv2.PSNR(noise_image_25,removed_noise_257))
PSNR for 5% noise image is 14.009187864798987 PSNR for 15% noise image is 9.959462219440645 PSNR for 20% noise image is 8.84052296004791 PSNR for 25% noise image is 7.948118565221031 PSNR for 25% with kernal size 7*7 noise image is 7.921379270139154
import helper
import cv2
# Loading Image
from skimage.io import imread,imshow,imsave
import numpy as np
from matplotlib import pyplot as plt
# Chnaging 256*256 image to 64*64
img = cv2.imread('cameraman.png',0)
output_64 = helper.reduce_image_size(img.copy())
nn_interpolation = helper.nearest_neighbour_interpolation(output_64,4)
(128, 128)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(output_64,cmap=plt.get_cmap('gray'))
plt.title("reduced image")
plt.subplot(1,3,3)
plt.imshow(nn_interpolation,cmap=plt.get_cmap('gray'))
plt.title("nn_interpolation")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# bilinear_interpolation
bilinear_interpolation = helper.bilinear_interpolation(output_64,4)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(output_64,cmap=plt.get_cmap('gray'))
plt.title("reduced image")
plt.subplot(1,3,3)
plt.imshow(bilinear_interpolation,cmap=plt.get_cmap('gray'))
plt.title("bilinear_interpolation")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
#cubic interpolation
cubic_interpolation = helper.cubic_interpolation(output_64,4)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(output_64,cmap=plt.get_cmap('gray'))
plt.title("reduced image")
plt.subplot(1,3,3)
plt.imshow(cubic_interpolation,cmap=plt.get_cmap('gray'))
plt.title("cubic_interpolation")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# Linear interpolation
linear_interpolation = helper.linear_interpolation(output_64,4)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(output_64,cmap=plt.get_cmap('gray'))
plt.title("reduced image")
plt.subplot(1,3,3)
plt.imshow(linear_interpolation,cmap=plt.get_cmap('gray'))
plt.title("linear_interpolation")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
# spline interpolation
spline_interpolation = helper.spline_interpolation(output_64,4)
# plot the images
plt.figure()
plt.subplots(figsize=(15, 5))
plt.subplot(1,3,1)
plt.imshow(img,cmap=plt.get_cmap('gray'))
plt.title("original")
plt.subplot(1,3,2)
plt.imshow(output_64,cmap=plt.get_cmap('gray'))
plt.title("reduced image")
plt.subplot(1,3,3)
plt.imshow(spline_interpolation,cmap=plt.get_cmap('gray'))
plt.title("spline_interpolation")
plt.tight_layout()
plt.show()
<Figure size 640x480 with 0 Axes>
img=img[:512,:512]
img.shape
(512, 512)
#psnr
print("PSNR for nn_interpolation is ",PSNR(img,nn_interpolation))
print("PSNR for bilinear_interpolation is ",PSNR(img,bilinear_interpolation))
print("PSNR for cubic_interpolation is ",PSNR(img,cubic_interpolation))
print("PSNR for linear_interpolation is ",PSNR(img,linear_interpolation))
print("PSNR for spline_interpolation is ",PSNR(img,spline_interpolation))
PSNR for nn_interpolation is 24.010932600998878 PSNR for bilinear_interpolation is 21.71993236161517 PSNR for cubic_interpolation is 33.16004251272271 PSNR for linear_interpolation is 32.988441393556734 PSNR for spline_interpolation is 33.15633465133061
# Save the image
imsave("noise_image_5.bmp",noise_image_5)
imsave("removed_noise_5.bmp",removed_noise_5)
imsave("noise_image_15.bmp",noise_image_15)
imsave("removed_noise_15.bmp",removed_noise_15)
imsave("noise_image_20.bmp",noise_image_20)
imsave("removed_noise_20.bmp",removed_noise_20)
imsave("noise_image_25.bmp",noise_image_25)
imsave("removed_noise_25.bmp",removed_noise_257)
imsave("nn_interpolation.bmp",nn_interpolation)
imsave("bilinear_interpolation.bmp",bilinear_interpolation)
imsave("cubic_interpolation.bmp",cubic_interpolation)
imsave("linear_interpolation.bmp",linear_interpolation)
imsave("spline_interpolation.bmp",spline_interpolation)
Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [0.0, 255.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [8.0, 250.0]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float64 to uint8. Range [8.0, 250.0]. Convert image to uint8 prior to saving to suppress this warning.